Since noises and biases in IMU measurements are unavoidable, even with proper sensor model, the accumulated drift in the integrated IMU poses will inevitably occur. To reduce the long-term pose drift, there are generally two families of algorithms: by fusing IMU readings with
measurements from other sensors [1]}, [2]}, [3]}, [4]} or relying on motion constraints of the platform where the IMU is installed on [5]}, [6]}, [7]}.
Representative sensors that are widely used in combination with IMU include global positioning system (GPS) [1]}, visual sensors [2]}, laser range finders [3]}, wheel odometers [4]}, and so on. On the other hand, if repetitive or special motion patterns are involved in the inertial navigation, they can also be probabilistically utilized to reduce the estimation errors. Typical motion patterns used in the navigation systems are zero velocity events [5]}, [6]} or pedestrian patterns [7]}.
There are also algorithms that utilize both measurements from complementary sensors and motion constraints to reduce pose errors [15]}, [16]}. For instance, Kottas et al. [15]} proposed a method on visual-inertial odometry by integrating a module of hovering motion detection, and VIMO [16]} designed a system that simultaneously estimates IMU poses and force using multi-sensory inputs.
